code: def _multilabel_recall_at_fixed_precision_arg_validation(
      num_labels: int,
      min_precision: float,
      thresholds: Optional[Union[int, List[float], Tensor]] = None,
      ignore_index: Optional[int] = None,
  ) -> None:
      _multilabel_precision_recall_curve_arg_validation(
          num_labels, thresholds, ignore_index
      )
      if not isinstance(min_precision, float) and not (
          0 <= min_precision <= 1
      ):
          raise ValueError(
              f"Expected argument `min_precision` to be an float in the [0,1] range, but got {min_precision}"
          )
  
file_path: torchmetrics/src/torchmetrics/functional/classification/recall_fixed_precision.py
imports: from typing import Callable, List, Optional, Tuple, Union
  import torch
  from torch import Tensor
  from typing_extensions import Literal
  from torchmetrics.functional.classification.precision_recall_curve import _binary_precision_recall_curve_arg_validation, _binary_precision_recall_curve_compute, _binary_precision_recall_curve_format, _binary_precision_recall_curve_tensor_validation, _binary_precision_recall_curve_update, _multiclass_precision_recall_curve_arg_validation, _multiclass_precision_recall_curve_compute, _multiclass_precision_recall_curve_format, _multiclass_precision_recall_curve_tensor_validation, _multiclass_precision_recall_curve_update, _multilabel_precision_recall_curve_arg_validation, _multilabel_precision_recall_curve_compute, _multilabel_precision_recall_curve_format, _multilabel_precision_recall_curve_tensor_validation, _multilabel_precision_recall_curve_update
  from torchmetrics.utilities.enums import ClassificationTask
label_desc: Use image node_id 9 for calling a global function with example usage: _multilabel_recall_at_fixed_precision_arg_validation(num_labels, min_precision, thresholds, ignore_index) without return types